U.S. patent application number 13/866731 was filed with the patent office on 2014-10-23 for circulation monitoring system.
This patent application is currently assigned to Semler Scientific, Inc.. The applicant listed for this patent is Semler Scientific, Inc.. Invention is credited to Paul Mannheimer, James McNames, Bob McRae, Doug Murphy-Chutorian.
Application Number | 20140316292 13/866731 |
Document ID | / |
Family ID | 51729544 |
Filed Date | 2014-10-23 |
United States Patent
Application |
20140316292 |
Kind Code |
A1 |
McRae; Bob ; et al. |
October 23, 2014 |
Circulation Monitoring System
Abstract
A peripheral arterial flow obstruction detection system for
providing a predictive diagnosis correlating to the diagnosis
peripheral arterial disease. The system includes a host computer
and a sensor used to detect and measure a physiological signal from
a subject's finger or toe, such as the measurement of a signal
using photoplethysmography using a PPG sensor. Sensor data is
processed and filtered before being used to calculate a number of
time-domain and frequency-domain calculations corresponding to the
signal waveform. The calculations are used in a predictive model
using a multi-faceted algorithm to provide a predictive diagnosis
that is displayed on an indicator such as a monitor.
Inventors: |
McRae; Bob; (Arvada, CO)
; Mannheimer; Paul; (Danville, CA) ; McNames;
James; (Portland, OR) ; Murphy-Chutorian; Doug;
(Palo Alto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Semler Scientific, Inc. |
Portland |
OR |
US |
|
|
Assignee: |
Semler Scientific, Inc.
Portland
OR
|
Family ID: |
51729544 |
Appl. No.: |
13/866731 |
Filed: |
April 19, 2013 |
Current U.S.
Class: |
600/504 |
Current CPC
Class: |
A61B 5/7275 20130101;
A61B 5/0261 20130101; A61B 5/7203 20130101; A61B 5/6829 20130101;
A61B 5/7257 20130101; A61B 5/6826 20130101; A61B 5/0295 20130101;
G16H 50/20 20180101; G16H 40/63 20180101 |
Class at
Publication: |
600/504 |
International
Class: |
A61B 5/0295 20060101
A61B005/0295; A61B 5/00 20060101 A61B005/00 |
Claims
1. A peripheral arterial flow obstruction detection system
comprising: a housing contoured to receive a portion of a
peripheral limb; a plethysmography signal sensor in the housing and
generating a pulse waveform output based on detected arterial flow;
a host computer operably coupled with said sensor; the host
computer configured to calculate values based on the pulse
waveform, including at least two calculations selected from the
group consisting of a time-domain feature calculation and a
frequency-domain feature calculation; where n has a value equal to
the sum of the number of time-domain feature calculations and
frequency-domain feature calculations performed by the host
computer, the host computer being further configured to calculate a
predictive diagnosis using the equation P ( Dx ) = 1 1 + ( C 0 + C
1 v 1 + C 2 v 2 + + C n v n ) , ##EQU00023## wherein P(Dx) is the
probability of flow obstruction; v.sub.1 through v.sub.n comprise
at least two calculations selected from the group comprising
time-domain feature calculations and frequency-domain feature
calculations; coefficients c.sub.0 through c.sub.n are
predetermined coefficients; and an indicator configured to display
the calculated predictive diagnosis.
2. The system of claim 1, wherein the sensor comprises a
photodiode.
3. The system of claim 1, wherein the sensor comprises a charge
coupled device (CCD).
4. The system of claim 1, wherein the physiological signal
comprises infrared light.
5. The system of claim 1, wherein the coefficients c.sub.0 through
c.sub.6 are determined through logistic regression.
6. The system of claim 1, wherein the plethysmography signal sensor
is a photoplethysmography sensor.
7. The system of claim 1, wherein the plethysmography signal sensor
is a volume plethysmography sensor.
8. A circulation obstruction detection system comprising: a
housing; a sensor in the housing positioned to receive a
physiological signal indicative of circulation; a host computer
operably coupled with said sensor to receive the physiological
signal and configured to analyze the physiological signal and
compute at least two calculations selected from the group
consisting of a) a time-domain feature calculation, and b) a
frequency-domain feature calculation; the host computer being
further configured to calculate a predictive diagnosis using said
at least two calculations, in a logistic function that predicts
probability of circulation obstruction; and an indicator configured
to display the calculated predictive diagnosis.
9. The system of claim 8, wherein the sensor comprises a
photodiode.
10. The system of claim 8, wherein the sensor comprises a charge
coupled device (CCD).
11. The system of claim 8, wherein the sensor comprises a pressure
cuff configured with a pressure transducer.
12. The system of claim 8, wherein the sensor comprises a strain
gauge.
13. The system of claim 8, wherein the physiological signal
comprises infrared light.
14. The system of claim 8, wherein the physiological signal
comprises organ volume.
15. The system of claim 8, wherein the physiological signal
comprises dermal temperature.
16. The system of claim 8, wherein the physiological signal
comprises dermal tension.
17. The system of claim 8, wherein the physiological signal
comprises blood velocity.
18. A peripheral arterial flow obstruction detection system
comprising: a housing contoured to receive a portion of a
peripheral limb, the housing further comprising a sensor capable of
detecting a photoplethysmographic signal from the portion of a
peripheral limb, the sensor generating a pulse waveform; a host
computer operably coupled with said sensor, the host computer
configured to obtain values based on the pulse waveform, including
the circulation index, harmonic slope, harmonic intercept, spectral
signal, and systolic rise; the host computer being further
configured to calculate a predictive diagnosis using the equation P
( Dx ) = 1 1 + ( C 0 + C 1 v 1 + C 2 v 2 + C 3 v 3 + C 4 v 4 + C 5
v 5 + C 6 v 6 ) , ##EQU00024## wherein P(Dx) is the probability of
flow obstruction; v.sub.1 is the circulation index (foot/handsMAX);
v.sub.2 is the harmonic slope (foot/handsMAX); v.sub.3 is the
harmonic intercept; v.sub.4 is the harmonic intercept
(foot-handsMAX); v.sub.5 is the spectral signal (foot/handsMAX);
and v.sub.6 is the systolic rise; coefficients c.sub.0 through
c.sub.6 are predetermined coefficients; and an indicator configured
to display the calculated predictive diagnosis.
19. The system of claim 18, wherein: c.sub.0 has a value ranging
from 15.99 to 20.11; c.sub.1 has a value ranging from -33.94 to
-38.76; c.sub.2 has a value ranging from 3.80 to 4.38; c.sub.3 has
a value ranging from -7.16 to -8.46; c.sub.4 has a value ranging
from 5.12 to 6.52; c.sub.5 has a value ranging from -2.14 to 3.28;
and c.sub.6 has a value ranging from 41.81 to 45.61.
20. The system of claim 19, wherein: c.sub.0 has a value of 18.05;
c.sub.1 has a value of -36.35; c.sub.2 has a value of 4.09; c.sub.3
has a value of -7.81; c.sub.4 has a value of 5.82; c.sub.5 has a
value of 2.71; and c.sub.6 has a value of 43.71.
Description
RELATED APPLICATIONS
[0001] This application relies on concepts drawn from U.S.
application Ser. No. 12/001,505 filed on Dec. 11, 2007, titled
CIRCULATION MONITORING SYSTEM AND METHOD, now issued as U.S. Pat.
No. 7,628,760 on Dec. 8, 2009, the contents and disclosure of which
are hereby expressly incorporated by reference in its entirety.
FIELD OF THE INVENTION
[0002] The invention relates generally to the field of medical
monitoring. More particularly, the invention relates to circulation
monitoring and signal processing to indicate a subject's
susceptibility to peripheral arterial disease, which is marked by
flow obstruction.
BACKGROUND OF THE INVENTION
[0003] Peripheral artery disease (PAD), as well as related coronary
heart disease (CHD) and carotid vascular disease (CVD), are
potentially fatal.
[0004] In the United States, an estimated 10 million people have
PAD. Approximately the same number are deemed undiagnosed due to
lack of symptoms and the relative inaccessibility of diagnostic
equipment. Disease endpoints for PAD are severe, i.e., disability,
limb amputation, and death. It is desirable to enable earlier
intervention and avoidance of many of the disease's more severe
outcomes by providing easier and more accessible tools to help
primary care physicians identify patients with PAD, CAD, CVD, and
other co-morbidities in the early stages of the disease.
[0005] While PAD is generally associated with lower extremity
atherosclerosis, it is associated with an elevated risk of CHD,
CVD, heart attack, stroke, and amputation. Approximately 75% of
patients having PAD also have CHD or CVD. Risk of stroke is three
times higher in patients with PAD than in those without the
condition. PAD manifests as stenosis or obstruction of the arteries
typically in the lower extremities and is caused by several
factors, including atherosclerosis, thrombosis, arterial
calcification, diabetes, and homocysteinemia. PAD is a progressive
chronic disease characterized by calf pain and disability,
specifically claudication, and restricted ambulation due to
critical limb ischemia. However, it should be noted that
approximately half of all patients with PAD were asymptomatic at
the time of their diagnosis.
[0006] Current diagnostic methods are typically applied to patients
who present with symptoms of claudication or leg pain at rest. A
common diagnostic pathway includes use of the Ankle-Brachial Index
(ABI) either at rest or post exercise, reactive hyperemia,
photoplethysmography (PPG), segmental blood pressure analysis,
pulse volume recording, duplex ultrasound, and peripheral
angiography.
[0007] The ABI is typically the first test deployed and is usually
performed in a physician's office or hospital vascular laboratory.
The ABI is calculated from observations of systolic blood pressures
taken from the brachial artery and at the ankle using
sphygmomanometers and Doppler ultrasound. Although the ABI is
considered the standard for non-invasive diagnosis of PAD, it is
time-consuming, awkward to deploy, subjective, and
technique-dependent. To obtain consistent and reliable results, the
practitioner must be highly experienced and have specialized
training. Further, the ABI is not a useful diagnostic in patients
with arterial calcification, a condition commonly encountered in
patients at risk for PAD. This fact is because ABI relies on
compression of stiff calcified arteries. Such a condition often
results in a false negative diagnosis.
[0008] Conventional photoplethysmography systems measure the
cardio-rhythmic volume of blood in a region of a subject's tissue.
Conventional pulse oximeters measure how much oxygen binds to
hemoglobin in red blood cells in a region of a subject's tissue.
Neither photoplethysmography nor pulse oximietry provide direct
correlation to blood flow or circulation quality.
SUMMARY OF THE INVENTION
[0009] The present invention relates generally to sensing
micro-vascular perfusion distal to an occlusion and subsequent
signal processing. More particularly, it involves a small finger or
toe format sensor having integral light generation and sensing
components that is operably connected to a host computing platform.
The host computer, which includes a processor and memory,
implements signal processing algorithms to assess the quality of
flow at distal locations of a body extremity.
[0010] The detection system includes a housing that is contoured
and adapted to fit comfortably over a finger or toe of a test
subject. Within the housing is a sensor, which is capable of
detecting and measuring a physiological signal. Physiological
signals being measured may include, for example, detecting arterial
blood flow using a plethysmography sensor. Other physiological
signals being measured may include infrared light, organ volume,
dermal temperature, dermal impedance, micro-vascular blood
velocity, or dermal tension. These signals may be measured a number
of different type of sensors, such as a photodiode, a charge
coupled device, a pressure cuff, electrodes or strain gauges.
[0011] The housing and the sensor are operably coupled to a host
computer. The host computer is configured to receive the data as a
waveform from the sensor and process it using signal processing
techniques. Examples of signal processing techniques include signal
artifact detection, normalization, signal filtering, and other time
and frequency-domain techniques.
[0012] From the sensor signal waveform, a number time-domain
feature and frequency-domain feature values that characterize the
signal waveform are calculated by the host computer. Examples of
the values calculated include a circulation index, harmonic slope,
harmonic intercept, systolic rise, spectral signal, spectral noise,
and spectral signal-to-noise ratios. Measurements are taken from
each limb of or at other various locations on a test subject.
[0013] Using the calculated values, the host computer generates a
predictive diagnosis using the calculated values as inputs. The
predictive diagnosis is calculated using a predictive model
equation that is specific to the type of the sensor used and the
type of signal gathered. The specific variables and coefficients
used in the predictive model equation are generated by performing
logistic regression on a sensor data obtained a sample group of
measurements made on test subjects with known diagnoses.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 shows the computer host connected to a sensor, placed
on an index finger of a test subject.
[0015] FIG. 2 shows the sensor placed on the second toe of a test
subject.
[0016] FIG. 3 shows an example a photoplethysmography (PPG)
waveform obtained from a patient with low physiological signal
amplitude coupled with a high level of low frequency noise.
[0017] FIG. 4 shows the corresponding spectral density diagram of
the PPG waveform in FIG. 3.
[0018] FIG. 5 is a process flow diagram for determining a
Pulsatility Index (PI) in accordance with one embodiment of the
invention.
[0019] FIG. 6 shows an example of a spectral power diagram of a
spectral density estimation of a PPG waveform for calculating
spectral signal and spectral noise variables.
[0020] FIG. 7 shows an example of a spectral power diagram of a
spectral density estimation of a PPG waveform for calculating the
harmonic decay variables.
[0021] FIG. 8 illustrates calculations of systolic rise period
variables based on the time-domain features of a PPG waveform.
[0022] FIGS. 9A and 9B represent a process flow diagram for a
prediction model based on a multi-faceted algorithm in accordance
with one embodiment of the invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0023] In one embodiment of the peripheral arterial flow detection
system, the system includes a host computer and a sensor for
collecting a physiological signal from a test subject or patient.
In another embodiment, the host computer may alternatively take the
form of a notebook computer, tablet, mobile smartphone, or a remote
server.
[0024] The host computer is configured to run a software
application, which displays data and allows the user to interact
with the system. In addition, the system includes a sensor
configured to interface with the host computer. The interface may
in one embodiment take the form of a hardwired connection through a
USB cable.
[0025] In another embodiment, the sensor may interface with the
host computer through any number of known wireless protocols, such
as BlueTooth or IEEE 802.11. Those skilled in the art will
recognize that the software application may run on the host
computer, within the sensor itself, or on a remote server over the
Internet.
[0026] In one embodiment, a physiological signal is collected from
a test subject or patient through the sensor. In one embodiment,
the physiological signal is a photoplethysmography (PPG) waveform
collected using a PPG sensor. It will be understood that while a
PPG waveform is in the form of a pulse waveform, use of different
types of sensors to collect other physiological signals will not
necessarily result in a pulse waveform.
[0027] Data from the sensor is transferred to the host computer for
processing. The host computer displays the data collected from the
sensor. The host computer may also calculate a number of values
based on the PPG waveform. Using the values characteristic of the
PPG waveform, the host computer is configured, such as through a
software application, to calculate a predictive diagnosis and
display the results on a monitor or display.
[0028] In addition, one of skill in the art will appreciate that a
variety of different sensor types may be used to collect a variety
of physiological signals, including not limited to
photoplethysmography (PPG), Laser Doppler Velocimetry (LDV),
infrared charged coupled device (CCD), a pressure cuff configured
with a pressure transducer, dermal impedance electrodes, and dermal
strain gauges.
[0029] With reference to FIG. 1, in one embodiment a host computer
110 is connected to a housing 120 by a USB cable 125. The host
computer 110 is running a software application that displays data
collected from the housing 120.
[0030] The housing is contoured to receive a portion of a
peripheral limb, such as a finger or a toe. Located within the
housing 120 is a sensor for detecting and measuring a physiological
signal. In one embodiment, the sensor is a PPG sensor that detects
and measures arterial flow and generates a photoplethysmographic
signal in the form of a pulse waveform.
[0031] It will be understood, as previously described, that the PPG
sensor may be substituted with other types of sensors for
collecting different types of physiological signals. It will also
be understood that the interface, shown as USB cable 125, may be
substituted with other wired or wireless data interfaces without
loss of functionality.
[0032] Although in FIG. 1 the housing 120 is shown as attached to
the index finger 130 of the left hand 135, the housing 120 may be
attached to other bodily appendages. For example, with reference to
FIG. 2, the housing 120 may be attached to the second toe 140 of a
test subject's right foot 145. As further described herein, it may
be desirable to take sensor readings from other appendages, organs,
or locations on the body, such as fingers, toes, feet, hands, legs,
ears, forehead, supraorbital, and epidermis.
[0033] In one embodiment, a netbook computer may be used as the
host computer. For example, the host computer may be configured to
run a Microsoft.RTM. Windows.RTM. based operating system or the
like. Typically, such a computer will include a central processor,
such as the Atom N455 CPU and include approximately 1 GB of random
access memory (RAM). In addition, such a computer will typically
include means to connect to external devices, including a network
card, a BlueTooth card, and universal serial bus (USB) ports.
Additionally, such a PC may include a small LCD display.
[0034] Alternative computing hosts may include a tablet, running
Apple's iOS, an Android tablet, or Windows tablet. Smaller host
computers facilitate portability in that they are easier for
healthcare practitioners to carry between patients.
[0035] In one embodiment, the software application may be written
in C#. Alternatively, several different languages may be suitable,
including: Java, MatLab, C, C++, AJAX, Javascript, Perl, Ruby,
Python, VB, and the like.
[0036] It will be understood that depending on what physiological
signal is being collected, different sensors can be used. In one
embodiment, the sensor is a plethysmography sensor for measuring
the relative blood flow through a finger or a toe. It will be
understood that plethysmography sensors refer to a class of sensors
for plethysmography, including for example photoplethysmography
(PPG) sensors, inductive plethsymography sensors, strain gauge
plethysmography sensors, and volume plethysmography sensors. In one
embodiment, the PPG sensor includes an infrared light emitting
diode (IR LED), operating at 940 nm paired with a photodiode
sensitive to a similar wavelength. It will be understood that other
wavelengths may also be used, such as 630 nm and 535 nm. The PPG
sensor may also include a microcontroller unit (MCU), which may be
programmed to perform many of the operations as described in this
specification. In another embodiment, the MCU of the PPG sensor may
serve the function of the host computer itself. Examples of
suitable MCUs are manufactured by Texas Instruments, such as the
MSP430 series MCUs. Other components may also be included in the
PPG sensor, including an analog to digital converter (ADC) such as
model LTC2451 from Linear Technologies or a digital to analog
converter (DAC) such as model LTC1669 from Linear Technologies.
[0037] The previously described PPG sensor may be used to provide a
data signal such as voltage output, current, or ADC bit counts.
This data signal may then be further processed by the host
computer.
[0038] Although an embodiment has been described using a PPG
sensor, it is understood by those of skill in the art that other
sensor types may be used for obtaining vascular physiological
signals. For example, other researchers have demonstrated the
utility of various sensor types for the purpose of monitoring
vascular perfusion, such as described in the following references,
the disclosures of which are hereby expressly incorporated by
reference in their entirety:
[0039] Laser Doppler Velocimetry: Fischer M, et al, "Simultaneous
measurement of digital artery and skin perfusion pressure by the
laser Doppler technique in healthy controls and patients with
peripheral arterial occlusive disease.", Eur J Vasc Endovasc Surg.
August 1995; 10(2):231-6. Such a system uses a laser to illuminate
a local region of epidermis and then measures the back-scattered
laser light. This back-scattered light undergoes a frequency shift
proportional to its velocity. Thus, the signal of interest may be
generated and recorded in terms of raw laser light, voltage,
current, or ADC counts which are related to the phase shift.
[0040] Infrared CCD: Karel J. Zuzak, et al, "Visible spectroscopic
imaging studies of normal and ischemic dermal tissue." Proc. SPIE
3918, Biomedical Spectroscopy: Vibrational Spectroscopy and Other
Novel Techniques, 17 (May 8, 2000). In the case of an infrared CCD,
the signal may be generated and recorded in the form of wavelength,
voltages, currents, or ADC counts.
[0041] Pressure cuff and transducer: Biomedix, Inc.
(http://www.biomedix.com/products/PADnet_plus.asp). Such a pressure
transducer may produce a current or voltage related to pressure or
pressure changes. These raw signals may be further digitized to
allow for further processing.
[0042] Impedance Plethysmography: Jane C Golden and Daniel S Miles,
"Assessment of Peripheral Hemodynamics Using Impedance
Plethysmography", PHYS THER. 1986; 66:1544-1547. As the name
implies, changes in skin impedance are measured with dermal
electrodes. The output signal is typically an electrical current,
which may be digitized through an ADC and relayed to the host
computer.
[0043] Strain Gauge Plethysmography: Myers, Kenneth, "The
Investigation of Peripheral Arterial Disease By Strain Gauge
Plethysmography", ANGIOLOGY July 1964 15: 293-304. This modality is
accomplished via a strain gauge affixed to the epidermis, typically
in an circumferential manner around the organ being measured. Thus,
arterial perfusion results in volumetric changes in the organ,
which tends to change the resistance of the strain gauge. This
analog voltage may be digitized and relayed to the host computer
for processing.
[0044] In each of the above mentioned technological modalities,
arterial perfusion is sensed and relayed to the host computer as a
signal for further processing.
Multi-Faceted Algorithm
[0045] The operation of the multi-faceted algorithm (MFA) will be
now be described. Although the description is provided in the
context of PPG waveforms collected from a PPG sensor from the
different limbs of a test subject or patient, it will be understood
that the methodology of the MFA can be used to analyze and derive
predictive diagnoses using physiological signal data collected at
various bodily locations using other types of sensors such as those
previously described.
[0046] Signal traces obtained from a sensor such as
photoplethysmography (PPG) can be analyzed for morphological
features. The morphological features in a PPG waveform are
indicative of a limb's disease state. Morphological features of a
PPG waveform may include time-domain features such as the systolic
rise and the normalized signal. Morphological features of a PPG
waveform may also include frequency-domain features such as the
harmonic slope, harmonic intercept, spectral signal, and spectral
signal-to-noise ratios. A variety of indices can also be calculated
from the PPG waveform, such as a Circulation Index (CI) as
described in U.S. Pat. No. 7,628,760 (the disclosure of which is
incorporated by reference in its entirety), and a Pulsatility Index
(PI) as described herein. Further calculations, as described
herein, can be computed from each of these features. The results of
those calculations, which characterize the PPG waveform, may be
used as input components in a MFA to predict the probability of
arterial flow obstruction.
[0047] The MFA takes into account not only the spectral flatness of
the PPG waveform, but other frequency and time-domain features of
the PPG waveform as well. Calculations generated from the
morphological features of the PPG waveform are used as inputs into
the MFA, which can be used to more reliably predict the probability
of flow obstruction than CI or PI alone.
[0048] Example of the types of calculations that can be generated
based on the morphological features of the PPG waveform are
described below.
Per Limb/Digit Measurements and Calculations
[0049] Data is may be collected through the sensor from each limb
or digit. Data is relayed from the sensor and analyzed either at
the end of a measurement or in real-time. In one embodiment, blood
volume measurements are sampled at a rate of approximately 50 times
per second. In one embodiment, the measurement of a digit may be of
a finite duration, such as 15 seconds, or may be a continuous
measurement with a user-determined termination.
[0050] Optionally, in real-time, on a periodic basis (e.g., once
per second), or at the conclusion of a digit measurement, the
measurement is assessed for the presence of a signal artifact.
Various methods of artifact detection may be used. Such methods
include: bispectrum/bicoherence analysis, DC discontinuity
assessment, kurtosis, and skew. In one embodiment, the Sample
Kurtosis method is used. The Sample Kurtosis method is represented
in Equation 1:
g 2 = m 4 m 2 2 - 3 = 1 n i = 1 n ( x i - x _ ) 4 ( 1 n i = 1 n ( x
i - x _ ) 2 ) 2 - 3 ##EQU00001##
[0051] Measurements that do not have a signal artifact are marked
with low kurtosis measure, such as less than 1.0. In one
embodiment, a kurtosis threshold of 1.0 is able to achieve a
sensitivity of 97% and specificity of 97% in the detection of
clinically relevant artifacts.
[0052] As the sensor data represents reflected light as opposed to
absorbed light, subtraction is a simple means of transforming the
signal data in a manner that represents absorbed light, which is
directly related to volumetric changes in the imaged arteriole bed.
In addition, further time-domain calculations rely on the proper
temporal orientation of the PPG waveform. In addition, a
normalization step may be performed which yields a signal that is a
direct measure of blood volume changes. It is computationally
efficient and convenient to subtract and normalize the signal with
a single mathematical equation such as in the following form
(Equation 2):
normData = 1 - rawData initialMedian , ( 2 ) ##EQU00002##
where, rawData is the data from the sensor and initialMedian is the
median of the initial raw data, typically three points. The initial
median is used to normalize rather than the initial point to allow
for initial transient conditions or other anomalies. It should be
recognize that other normalization techniques may be used such as
initial average (of the first n-points), moving average of m-points
(e.g., 1-second), overall average, or a fixed nominal value, such
as the data associated with a nominal level (e.g., 32,768 ADC
points from a 16-bit ADC).
[0053] In addition, other subtraction techniques may be used such
as, initial median (of the first n-points), initial average (of the
first m-points), moving average (e.g., 1-second), overall average,
fixed nominal (e.g., 32,768 ADC points from a 16-bit ADC) or fixed
maximum (e.g., 65,536 ADC points from a 16-bit ADC).
[0054] Normalizing the signal by an amount representative of the
average signal level (DC) yields a peak-to-trough (AC/DC
component), which may be clinically relevant. It should be
appreciated that doubling the amount of average light from the
illumination source would be accompanied by a doubling of the raw
AC component if it was not otherwise normalized by the DC value,
demonstrating the need for the application of a dynamic
normalization technique such as that provided by the initial
median.
[0055] After the normalization and subtraction, a normalized
spectral density of the signal is estimated using a modified
version of the Welch's Method. In particular, each spectral density
is normalized by dividing each frequency's power by the maximum for
that spectral density estimation. This technique reduces the
effects of noise, which tend to overwhelm the physiologic signal in
absolute power, but due to the transient nature are mitigated by
the averaging associated with the Welch Method and the addition of
normalization to the method.
[0056] For example, assume a physiologic signal occurs at 1 Hz and
5,000 power units and presents relatively consistently through each
Fourier Fast Transform (FFT) of the Welch Method. However, if noise
presents in only a few of the many FFT's with a power of 10,000
units at a frequency of 0.5 Hz, normalizing transforms that power
to 1.0 unit. In addition, noise may only be present in a fraction
of the FFT's used in the Welch Method. Due to the noise mitigating
nature, normalizing the spectral density allows for a more reliable
estimate of the Fundamental Frequency (F0).
[0057] Several methods exist for estimating F0, including:
prominence, simple maxima, Harmonic Product Spectrum (HPS) and
variants thereof. Strong signals which are devoid of noise allow
for simple techniques such as simply selecting the maximum power
associated with a spectral density. Such selection may be further
enhanced and computational efficiencies gained by narrowing the
frequency range to, say, 0.6 to 2.5 Hz.
[0058] When physiologic signals become confounded with noise, more
sophisticated techniques become necessary to better estimate F0.
The HPS is one such method, which involves essentially a product of
the powers of a given frequency and its associated harmonics. For
example, the power of 1.2 Hz is multiplied by the power of 2.4 Hz,
3.6 Hz, 4.8 Hz and so on and so forth. A variant of this method
includes sums instead of products.
[0059] Another method, which has shown particular utility involves
the use of the prominence, or relative height, of the spectral peak
as compared to surrounding points. For example, the prominence of
1.2 Hz may be calculated as the ratio of power at 1.2 Hz to the
average of powers at 0.8 Hz and 1.6 Hz. Prominence shows particular
advantage in spectra which include low-frequency noise where the
power is high at low frequencies, but decays as frequency
increases. In such a case, the HPS does not differentiate a local
peak as well as the prominence method. The width of adjacent
comparison (spectral "distance" from the center frequency) is an
important parameter in the prominence method.
[0060] One choice for a denominator comparison is 1/2 F0, which
should theoretically correspond to the troughs to F0's peak.
Empirical testing with noisy clinical data demonstrated that a
width 35% was optimal. For example if the presumed F0 was 1.2 Hz,
the adjacent comparison frequencies were 0.78 Hz and 1.62 Hz.
Equation 3 below shows the calculation of prominence:
.pi. ( f ) = 2 P ( f ) P ( f - .lamda. ) + P ( f + .lamda. ) , ( 3
) ##EQU00003##
where P(f) is the power at a given frequency and a, is the offset
width. Selection of F0 is based on the associated frequency with
maximum prominence.
[0061] It should be understood that combinations of prominence and
HPS are to be considered within the scope of this invention.
[0062] Once an estimate of F0 has been made, the signal may then be
filtered to remove low-frequency components with minimal alteration
of the signal of interest. That is, by knowing the F0, one may then
perform a high-pass filter with a corner frequency that minimally
attenuates the F0, but maximally attenuates frequencies below
F0.
[0063] Performing a high-pass filter essentially de-trends the
signal and provides for more reliable time and frequency-domain
processing on the signal of interest. Several different methods may
be used for high-pass filtering in both the time and
frequency-domains, including: Butterworth, Chebyshev, moving
average, and convolution.
[0064] For example, a 4.sup.th order, bidirectional Butterworth
filter provides for a sharp attenuation, zero phase shift, and is
reasonably computationally efficient and stable. In addition, it
provides a flat frequency response in the band-pass range, which is
particularly important for further frequency-domain processing
which relies on a relatively unadulterated signal. Selecting a
corner frequency of approximately 40% of F0 tends to limit
attenuation of F0 to around 5%, but effectively attenuates
components due to such things as: involuntary movement, ambient
light changes, and respiration.
[0065] As described in U.S. Pat. No. 7,628,760, the Circulation
Index (CI) is a measure of how strong the quasi-periodic component
of the signal, which is essentially the compliment of the Spectral
Flatness Measure (SFM). Like the SFM, the CI is on a scale of 0 to
1, though it is may sometimes be expressed as a percentage (e.g.,
0% to 100%). Although SFM has been used in speech processing and
other applications, SFM has not been used in cardiovascular signal
applications except in limited circumstances such as those
described in U.S. Pat. No. 7,628,760.
[0066] Calculation of CI may be improved by removing the
low-frequency components of the signal via a variety of detrending
methods including: high-pass filtering, linear regression,
polynomial fitting, cubic spline fitting, or smoothness priors.
Removal of the low-frequency components which are typically
associated with physiologic processes and/or environmental
conditions not related to volumetric changes in arteriole
circulation. That is, respiration, involuntary movements, and
ambient light changes may be essentially removed from the signal as
a preprocessing step and yield a more clinically significant
estimate of the CI.
[0067] Although the Spectral Flatness Measure (SFM) is normally
defined over the entire frequency range of the PSD, it can also be
applied to any band of frequencies. In particular, one embodiment
includes the frequency range of 0.6 Hz to 8.0 Hz, which captures
the fundamental frequency and the corresponding three harmonics of
most patients.
[0068] An alternative embodiment uses a Pulsatility Index (PI)
instead of or in addition to the CI. As described previously and in
U.S. Pat. No. 7,628,760, the CI is correlated with and can be used
as a standalone metric to predict the probability of a positive PAD
diagnosis.
[0069] In some patients, the spectral flatness of the PPG waveform
alone may not provide a reliable prediction of a PAD diagnosis. In
patients with proximal flow obstruction the PPG waveform may show
low physiological signal amplitudes coupled with a high level of
low frequency noise. Patient respirations, arrhythmia,
low-frequency blood flow changes, or other ambient light and motion
artifacts may also affect the spectral flatness of the PPG
waveform.
[0070] An example of such a PPG waveform taken from the left foot
of a test subject is shown in FIG. 3. The PPG waveform 210 is
plotted as amplitude over time. As can be observed, the PPG
waveform 210 displays a low physiological signal amplitude that is
coupled with a high level of low frequency noise. This is more
evidence when viewing the corresponding power spectral density
diagram for the PPG waveform 210.
[0071] As can be seen by the corresponding spectral density diagram
in FIG. 4, the waveform is characterized by the prominence of a low
frequency peaks 225. This low frequency power shows the presence of
a high level of noise. In these types of patients, predictions
calculated from the spectral flatness measure alone using methods
such as the Circulation Index (CI) described in U.S. Pat. No.
7,628,760 may provide distorted results. This is because the low
frequency noise results in a low spectral flatness measure, and as
a result an abnormally high CI.
[0072] An alternate algorithm may be used in order to overcome such
limitations of the spectral flatness based CI as a sole predictor
of flow obstruction. This alternate algorithm, referred to as the
Pulsatility Index (PI), will now be described.
[0073] The Pulsatility Index (PI) addresses this issue by
considering the spectral power at the fundamental frequency and its
harmonics, compared to the power in the troughs between to
marginalize non-physiologic signal noise.
[0074] FIG. 5 shows the main process steps of one such algorithm
for determining a Pulsatility Index. Beginning with a signal x(n)
shown as 310, the first step in determining the Pulsatility Index
is to detrend the signal as shown in step 320. This can be
accomplished by estimating the trend of the waveform and removing
it. The trend may be estimated using any number of methods,
including but not limited to: smoothness prior method, linear
regression, polynomial fitting, use of a cubic spline, or
application of a high-pass or low-pass filter. In one embodiment,
the trend of the waveform is estimated using a finite impulse
response (FIR) low-pass filter to estimate the trend, as shown in
Equation 4:
x t ( n ) = k = - M M h M ( n - k ) x ( k ) , ( 4 )
##EQU00004##
where M is the length of the impulse response h(n), n is the
discrete-time index, x(k) is the input signal, and x.sub.t(n) is
the output. In one embodiment, the impulse response have equal
weightings. In another embodiment, an Epanechnikov kernel is used
as the impulse response.
h M ( n ) = { 1 - ( n M ) 2 n < M 0 n .gtoreq. M , ( 5 )
##EQU00005##
[0075] The width of the kernel is specified in terms of the cutoff
frequency. In one embodiment, the algorithm uses a cutoff frequency
of 0.5 Hz. At 0.5 Hz, the cutoff frequency is low enough to avoid
inclusion of the cardiac cycle in the estimate of the trend, but
high enough to include the additive effects of respiration and
other elements that affect low frequency drift. Other embodiments
may use a dynamic corner frequency based on an estimate of F0. A
Fast Fourier Transform (FFT) algorithm can be used to reduce the
computational cost of the algorithm. In one implementation, the
first and last portions (e.g., 0.5 seconds) are truncated to
eliminate the edge effects of the filter.
[0076] The detrended signal is then calculated simply as the
subtraction of the low-pass signal (the trend) from the original
signal:
x.sub.d(n)=x(n)-x.sub.t(n) (6),
[0077] After the signal is detrended, the spectral density of the
signal is estimated as shown in step 330. Any number of methods may
be used to estimate the spectral density, including, but not
limited to: Power Spectral Density (PSD), Welch's Method, and
Burg's Method. In one embodiment, the spectral density estimation
is the Power Spectral Density (PSD). The PSD can be estimated using
parametric or nonparametric methods. In one implementation, the PSD
is estimated using a Blackman-Tukey spectral estimation. The
autocorrelation of the signal is estimated using the standard,
biased estimator, as shown in Equation 7:
r ^ x ( ) = 1 N n = 0 N - - 1 x d ( n + ) x d ( n ) , ( 7 )
##EQU00006##
where N is the length of the detrended segment, the segment is
indexed from 0 to N-1, and f is an index indicating the delay at
which the estimate is calculated.
[0078] The autocorrelation is then multiplied by a window
w.sub.a(l) and the discrete time Fourier transform of the windowed
autocorrelation function is calculated. Any number of windowing
techniques may be used, including, but not limited to: Blackman,
Hamming, Hanning, etc. In one embodiment, a Blackman is used, as
shown in Equation 8:
R ^ x ( .omega. ) = = - ( L - 1 ) L - 1 r ^ x ( ) .omega. a ( ) - j
.omega. , ( 8 ) ##EQU00007##
where .omega. is an index of frequency. A window should be chosen
so that there is sufficient duration to estimate peaks in the PSD
due to the cardiac cycle, but also provides sufficient smoothing to
significantly reduce the variance of the estimate and eliminate
spurious peaks. In one implementation, the Blackman autocorrelation
window has a duration of 8 seconds.
[0079] After the PSD has been estimated, the heart rate can be
estimated as shown in step 340 by searching over a range of
possible heart rate frequencies for the heart rate that optimizes a
criterion. In one embodiment, searches are made over the frequency
range of 0.7 to 2.5 Hz, which corresponds to 42 to 150 beats per
minute. The criterion is based on the sum of the powers at the
harmonics of the candidate heart rate relative to the powers of the
PSD in the troughs between the harmonics. This method is a variant
of the prominence method, as previously described. The power at the
harmonics is calculated as the value of the PSD at integer
multiples of the peaks, as shown in Equation 9:
p p ( .omega. ) = k = 1 N h R ^ x ( k .omega. ) , ( 9 )
##EQU00008##
where N.sub.h is the number of harmonics used. One embodiment uses
the fundamental (F0) and three harmonics (F1, F2, F3). The power of
the harmonic signal could be estimated with other methods, such as
calculating the area of each peak with close, surrounding
neighboring frequencies.
[0080] The power of the troughs is calculated as the value of the
PSD at frequencies in between the harmonics. In one implementation,
three frequencies in between each pair of harmonics are used,
specifically the midpoint (50% inter-peak point), the 40%
inter-peak point, and the 60% inter-peak point, as shown in
Equation 10:
p t ( .omega. ) = k = 1 N h R ^ x ( ( k + 0.4 ) .omega. ) + R ^ x (
( k + 0.5 ) .omega. ) + R ^ x ( ( k + 0.6 ) .omega. ) , ( 10 )
##EQU00009##
[0081] The criterion for determining the heart rate is calculated
as the ratio of the sum of the PSD power in the troughs divided by
the sum of the PSD power at the peaks as shown below in Equation
11.
.rho. ( .omega. ) = p t ( .omega. ) p p ( .omega. ) , ( 11 )
##EQU00010##
[0082] The candidate heart rate with the smallest ratio is chosen
as the estimated heart rate, as shown in Equation 12:
.omega..sub.hr=argmin .rho.(.omega.) (12),
where function argmin refers to the index associated with the
smallest value of p(w).
[0083] After the heart rate has been estimated, the Pulsatility
Index can be calculated in step 350 as shown in Equation 13:
b = { 1 - .rho. ( .omega. hr ) .rho. ( .omega. hr ) .ltoreq. 1 0
.rho. ( .omega. hr ) > 1 , ( 13 ) ##EQU00011##
where .rho.(.omega..sub.hr) is the ratio described in the earlier
stage evaluated at the estimated heart rate. The Pulsatility Index
is represented by the reference character b 360 in FIG. 5.
[0084] Small ratios corresponding to high power at the peak
frequencies result in large estimates of pulsatility. Large ratios
that correspond to more power in the troughs and less power at the
peaks return results that correspond to small estimates of
pulsatility.
[0085] Returning specifically to the MFA, after low-frequency
components have been removed through de-trending, a traditional
spectral density estimation may be performed which is not dominated
by low-frequency noise. Again, various methods may be used as
previously discussed, including the traditional Welch Method. This
spectral density estimation may be particularly useful for a more
accurate estimation of F0. Typically, the peak power between a
certain frequency range such as 0.6 Hz to 2.5 Hz may correspond to
F0. Again, various methods of estimating F0 may be used. In one
embodiment, the prominence method is used.
[0086] The spectral density estimation provides the basis for the
calculation of such variables as spectral signal, spectral noise,
and spectral signal-to-noise ratio (SNR). FIG. 6 shows a spectral
power diagram 400 of a spectral density estimation of a PPG
waveform 405. From the spectral power diagram 400, the fundamental
frequency 410 (F0), as well as the first harmonic 415 (F1), second
harmonic 420 (F2), and third harmonic 425 (F3) can be identified
from the estimated spectral density of a PPG waveform 405.
[0087] The spectral signal ("spec.sig") and the spectral noise
("spec.noise") can be calculated from:
spec . sig = i = 0 N P i , ( 14 ) spec . noise = ( N + 1 N + 2 ) j
= 0 N + 1 P j , ( 15 ) ##EQU00012##
where P.sub.i is the power at F0, F1, F2, . . . Fn, and P.sub.j is
the power at F0/2, 3F0/2, 5F0/2, . . . (N+1)F0/2, which generally
corresponds to the "x" marks 412 in FIG. 6.
[0088] The Spectral SNR ("spec.SNR") is calculated as shown in
Equation 16 below. It should be recognized that the Spectral SNR
shares many similarities with the PI as previously described.
spec . SNR = spec . sig spec . noise , ( 16 ) ##EQU00013##
[0089] With reference to FIG. 7, another example of a spectral
power diagram 450 of a spectral density estimation of a PPG
waveform 455 is shown. Again, the fundamental frequency 460 (F0),
as well as the first harmonic 465 (F1), second harmonic 470 (F2),
and third harmonic 475 (F3) can be identified from the estimated
spectral density of a PPG waveform 455.
[0090] Having identified the fundamental frequency and harmonic
frequencies, the Harmonic Decay (HD) can be defined using Equation
17:
HD=Ae.sup.-bf (17),
where A is the harmonic intercept, b is the harmonic slope, and f
is either the harmonic number (0, 1, 2, 3, etc) or the actual
corresponding frequency (1.2, 2.4, 3.6, 4.8 Hz, etc.). Either the
harmonic number or the actual frequency may be used in the
calculation of the Harmonic Decay variables. In some instances, the
harmonic number actually provides for a more statistically
significant variable than the absolute frequency.
[0091] The harmonic slope ("harm.slope") can be calculated using a
standard least squares approach or the LINEST and INDEX functions
used in the commercially available software package Microsoft.RTM.
Excel.RTM. using the formula below:
harm . slope = INDEX ( LINEST ( ln ( P Fi P F 0 ) ) , 1 ) i = 0 , 1
, 2 , 3 , n , ( 18 ) ##EQU00014##
where P.sub.F0 is the power of the fundamental frequency, and
P.sub.Fi is the power of the ith harmonic of F0, the first harmonic
(P.sub.F1), the second harmonic (P.sub.F2), etc. While any number
of harmonics may be used, sufficient clinical utility may typically
be achieved using up to the third harmonic (i=3). The function
above shows the harmonic power being normalized with the power of
F0 (e.g., P.sub.Fi/P.sub.F0). An alternative embodiment excludes
this normalization.
[0092] Similarly, the harmonic intercept ("harm.int") can be
calculated using the formula below:
harm . int = INDEX ( LINEST ( ln ( P Fi P F 0 ) ) , 2 ) i = 0 , 1 ,
2 , 3 , n , ( 19 ) ##EQU00015##
Again, a typical least squares function may be used in place of the
above Microsoft.RTM. Excel.RTM. function. As well, the harmonic
intercept may or may not be normalized with the power of F0.
[0093] In addition to accurate Harmonic Decay variables, the
de-trended signal also provides the basis of an accurate estimate
of F0, which is critical in further time and frequency-domain
calculations. Again, several methods may be used to estimate F0 as
previously described. One implementation uses the prominence
method, as described in Equation 3.
[0094] Once a reliable F0 has been estimated, the signal may be
further filtered to remove high-frequency components, which lie
beyond the signal of interest. Again, several methods may be used
to remove the high frequency components in both the time and
frequency-domains, including: Butterworth, Chebyshev, moving
average, and convolution. One implementation, for example, uses a
4.sup.th order, bidirectional Butterworth low-pass filter with a
corner frequency of 4.5 times F0. For example, if F0=1.2 Hz the
corner frequency would be set at 5.4 Hz. Selection of this corner
frequency provides to an unaltered signal between F0 and the
"trough" following the third harmonic. In general, selection of the
corner frequency may be based on the following equation:
F.sub.c=(HN+1+0.5).times.F0 (20),
where HN is the harmonic number (e.g., 3).
[0095] Attenuation of the higher frequency components provides for
a signal which includes only the frequency components of interest.
Removal of the high-frequency noise, for example, provides a more
reliable means of peak and trough detection, or simply "peak
detection."
[0096] In addition to the frequency-domain features, a PPG waveform
will have a number of time-domain features.
[0097] Robust peak detection is critical for a reliable calculation
of the Systolic Rise (SR) variable. While a noisy signal benefits
from aggressive filtering, the filter specifications should be
selected with care to avoid significantly altering the shape of the
signal. Hence, selection of the frequency range from 0.40 to 4.5
times F0 provides for a reasonably filtered signal allowing for
robust peak detection without distorting the signal of
interest.
[0098] The SR feature of the PPG waveform will be discussed with
reference to FIG. 8. The SR of a waveform 510 may be expressed as a
percentage of the period 520 (P) and is calculated as:
SR = S P , ( 21 ) ##EQU00016##
where S is the time 525 it takes the waveform to rise from trough
to peak and P is the period of the signal. Alternatively, SR may be
expressed in absolute terms.
[0099] Systolic rise is correlated with PAD in the measured limb,
with diseased limbs having longer SR periods. Proper determination
of SR is contingent on the determination of the proper peaks and
troughs in the waveform. In one implementation, the peak is
detected by first estimating an initial, candidate SR based on the
largest pseudo-slope
y 2 x , ##EQU00017##
where dy is the change in pulse amplitude and dx is change in time,
through various run distances dx from 60-miliseconds through the
estimated period (P0), which his derived from the estimated F0. A
pseudo-slope array is then generated, with the pseudo-slope array
representing
y 2 x ##EQU00018##
with a fixed run (dx) based on the candidate SR. The pseudo-slope
array is used to estimate the center of the SR feature of the PPG.
It is this feature (systolic slope), which is used, rather and a
single point to find the peaks and troughs.
[0100] Peaks and troughs are then located by reviewing the PPG
waveform with a center index defined as the maxima of the
pseudo-slope array. First, the first local maxima of
y 2 x ##EQU00019##
is located, corresponding to the first systolic slope feature.
Then, the trough is identified by maximizing the argument
y 2 x , ##EQU00020##
indexing dx from the center index. Once a minima is found and
confirmed, the corresponding maxima is sought. The pair of minima
and maxima corresponds to the trough and peak, respectively, and
are added to a peak array. The next center index is then
incremented by one period. Then that center index is adjusted to
correspond with the maxima slope in the slope array within .+-.P0/2
(within one half of a period). The process is repeated throughout
the entire PPG waveform to generate a peak array identifying the
pair of troughs and peaks for each period in the PPG waveform.
[0101] In summary, peaks may be identified according to the
following criteria: [0102] occurs in pairs [0103] occur nominally
at a distance of P0 [0104] surround the steepest, longest rise
portions of the signal (the Systolic Rise period 525,
M2=dy.sup.2/dx) [0105] troughs are identified by:
maxarg.sub.x(dy.sup.2/dx) from centers of M2 [0106] troughs are not
on edges of signal array [0107] peaks occur after troughs [0108]
peaks are>surround two points [0109] distance (d) between trough
& peak is: 15% P0.ltoreq.d.ltoreq.50% P0
Comparison Calculations
[0110] Further diagnostic accuracy may be achieved with subsequent
limb or digit measurements and variable calculations. For example,
comparisons of the same calculation taken from the toe to a finger,
or from a left toe to right toe, may yield relative variables which
are clinically indicative of disease. As an example, the
Circulation Index (CI) of the toe may be compared to the hand in
various fashions such as a ratio (e.g., CI.sub.toe/CI.sub.finger)
or difference (e.g., CI.sub.toe-CI.sub.finger). Further, a
toe-to-finger variable may use various bases (e.g., divisor or
subtrahend) such as: average, "best", maximum, or minimum (e.g.,
CI.sub.left-toe/CI.sub.AVG(fingers) or
CI.sub.left-toe/CI.sub.MAX(fingers)). Additionally, a given toe may
be compared to the contralateral toe or the average of the toes
(e.g., CI.sub.left-toe/CI.sub.AVG(toes)). It should be recognized
that each variant described here may be applied to each per-limb
variable (e.g. CI, PI, Harmonic Slope, etc.).
[0111] The "best" basis is simply the hand, position of an organ,
or limb in general, which is selected based on a particular
quality. For example, the best hand might be selected based on the
maximum SNR or CI among the two hands. Therefore, that hand is used
as the basis for each comparison calculation.
[0112] Therefore, several variables may be used to estimate the
probability of flow obstruction of a given limb by using the
absolute limb variables (e.g., CI.sub.left-toe) in addition to the
relative limb variables (e.g.,
CI.sub.left-toe/CI.sub.AVG(fingers)).
Predictive Model
[0113] A prediction model for flow obstruction may be generated
using the absolute and relative variables. One implementation uses
a logistic function of the following form:
P ( Dx ) = 1 1 + ( C 0 + C 1 v 1 + C 2 v 2 + + C n v n ) , ( 22 )
##EQU00021##
where P(Dx) is the probability of flow obstruction, v.sub.1,
v.sub.2, . . . v.sub.n are the absolute and relative variables
based on the calculations as discussed above, and C.sub.0, C.sub.1,
C.sub.2, . . . C.sub.n are the coefficients corresponding to each
variable.
[0114] In order to determine the predictive model specific for the
type or model of sensor, sensor data is taken, for example, from
each limb from a sample population of test subjects with known
diagnoses. Using the techniques described above, the signals from
each limb are processed to calculate per-limb variables and
comparative variables characterizing the sensor data as described
above. Using a logistic regression model performed on aggregate
data from normal and abnormal limb measurements, the coefficients
for each variable are determined.
[0115] It should be noted that the coefficients for some variables
may be zero, meaning the variable is not statistically significant
and may be omitted. Equation 22 yields values ranging from 0.0 to
1.0 with values below a certain threshold (e.g., 0.5) suggesting
the limb is flow obstructed.
[0116] The aforementioned threshold may be determined based on a
sensitivity analysis, considering the sensitivity, specificity, and
accuracy. In one embodiment, the sensitivity may be favored over
specificity. In another embodiment, the threshold may be set based
on maximizing the accuracy.
[0117] It is understood that the value of the coefficients and
threshold are readily determinable by one of skill in the art using
the above described methodology.
[0118] As a specific example, one embodiment uses the following
equation for the prediction of flow obstruction:
P ( Dx ) = 1 1 + ( C 0 + C 1 v 1 + C 2 v 2 + C 3 v 3 + C 4 v 4 + C
5 v 5 + C 6 v 6 ) , ( 23 ) ##EQU00022##
where the variables used and the corresponding coefficients include
the following values and 95% confidence interval:
TABLE-US-00001 (24) Coefficient Variable Name Value (95% CI)
C.sub.0 Intercept 18.05 (.+-.2.06) C.sub.1 CI (foot/handsMAX)
-36.35 (.+-.2.41) C.sub.2 Harm.Slope(foot/handsMAX) 4.09 (.+-.0.29)
C.sub.3 Harm.Int -7.81 (.+-.0.65) C.sub.4 Harm.Int(foot-handsMAX)
5.82 (.+-.0.70) C.sub.5 Spec.Sig(foot/handsMAX) -2.71 (.+-.0.57)
C.sub.6 Syst.Rise 43.71 (.+-.1.90),
[0119] In one embodiment, the sensor may include memory, which is
configured so that the relevant coefficients are loaded into memory
and downloaded to the host computer. The relevant coefficients and
variables calculated for the predictive model may be different
depending on the type or model of the sensor connected to the host
computer. In another embodiment, the relevant coefficients are
loaded into memory and downloaded to the host computer through a
memory card inserted into the host computer. In yet another
embodiment, the relevant coefficients are loaded into memory and
downloaded to the host computer through a USB drive. In another
embodiment, coefficients are loaded to the host computer from a
remote server.
[0120] FIGS. 9A and 9B show a process flow diagram illustrating the
operational steps of the multi-faceted algorithm in one example
implementation for a patient with four functional limbs, namely a
right arm, left arm, right foot, and left foot. In this example, a
PPG sensor connected to a host computer is attached to each limb in
turn to allow the PPG sensor to obtain data.
[0121] For each limb, the PPG sensor acquires a data signal as in
step 710. The host computer, or alternatively the sensor itself,
then identifies any signal artifacts as shown in step 712. Signals
are then subtracted and normalized as shown in step 714. As shown
in step 716, a normalized spectral density is estimated for the
acquired sensor signal, and fundamental frequency and harmonics are
identified in step 718. The signal is processed through a high-pass
filter in step 720 to remove low frequency noise components.
[0122] After the low frequency noise has been removed, the signal
may be processed in a number of different ways to calculate
different variables. In one path, the Circulation Index (CI) is
calculated in step 730 to yield a CI for the particular limb being
evaluated in step 732.
[0123] Although not explicitly shown in FIGS. 9A and 9B, it will be
understood that the PI may be calculated for each in place of or in
addition to the calculation of the CI.
[0124] The signal may also be processed to estimate the absolute
spectral density of the PPG waveform as in step 740. From this
step, the spectral signal, spectral noise, and spectral signal to
noise ratios can be calculated as shown in step 742. From the same
information, the harmonic slope and harmonic intercept may also be
calculated as shown in step 744.
[0125] From step 740, the fundamental frequencies of the spectral
density can be determined in step 750. The signal can then be
passed through a low pass filter in step 760 from which the AC
amplitude of the signals can be calculated in step 762. The low
pass filter step serves to attenuate higher frequency components of
the signal to facilitate reliable peak and trough detection in step
770.
[0126] Once peak and troughs have been detected in step 770, the
systolic rise period can be calculated in step 772 as previously
described.
[0127] This process is repeated for each of the four limbs on a
patient. Once data from all four limbs have been collected, the
host computer calculates the comparative variables such as those
shown in steps 780, 782, 784, 786, 788, and 790.
[0128] Having calculated the value of the comparative variables,
the host computer then calculates a predictive diagnosis using the
calculated comparative variables and a set of predetermined
coefficients. The predictive diagnosis may be displayed on an
indicator, such as a monitor attached to or integral to the host
computer.
[0129] Those of skill in the art will appreciate that certain
illustrated functional blocks can be omitted, reordered, combined,
or separated, within the spirit and cope of the invention.
Similarly, those of skill will appreciate that certain illustrated
software steps can be omitted, reordered, combined, or separated,
also within the spirit and scope of the invention. All such
suitable topologically and logically suitable alternatives to the
process flow diagramed in FIGS. 9A and 9B are contemplated as being
within the spirit and scope of the invention. Further, while
illustrated as being implemented via software, such steps may be
suitably implemented via hardware and/or firmware.
[0130] Those of skill in the art will also appreciate that while
the above steps are described in specific terms for a PPG sensor,
different sensor technologies may be used. Additionally, while the
examples provided above indicate the use of hands and feet, or
fingers and toes, other bodily organs maybe interrogated in the
same manner.
Experiment
[0131] In one experiment using a PPG sensor, the objective of the
experiment was to develop an algorithm predictive of peripheral
arterial flow obstruction (FO) using data from a PPG sensing
device.
[0132] Raw data from a PPG sensor was collected from 70 patients.
Fifty-five patients presented to five vascular centers with signs
or symptoms suggestive of peripheral arterial disease. Each patient
was tested with a PPG device bilaterally on each index finger &
each second toe and subsequently assessed with a proven imaging
modality (e.g., Duplex ultrasound, angiography, etc.). In addition,
fifteen normal control subjects were measured in the same fashion,
but without the additional imaging modality.
[0133] The raw data files from the PPG sensor were then analyzed
with a software application that performed calculations of several
different variables including: the Circulation Index (CI), Systolic
Rise (SR), Harmonic Slope (HS), Harmonic Intercept (HI), Spectral
Signal (SS), Spectral Noise (SN), Spectral Signal-to-Noise Ratio
(SNR). In addition, relative variables were calculated for the toes
as compared to the fingers. Limbs with FO were assigned a diagnosis
value of 1; while those absent FO were assigned a 0. This data was
then evaluated with XLStat (version 2012.5.02) using a logistic
regression analysis in a Monte Carlo simulation. That is, 107 limbs
were selected at random, a logistic model created, and then
validated against the other 30 limbs.
[0134] From 70 subjects and 137 limbs, 74 limbs had flow
obstruction and 63 were absent of flow obstruction. The variables
of significance included those listed in Equation 24: CI
(foot/handsMAX), Harm.Slope(foot/handsMAX), Harm.Int,
Harm.Int(foot-handsMAX), Spec.Sig(foot/handsMAX), and Syst.Rise.
The median accuracy in the prediction of FO was 89.7%.
[0135] It will be understood that the present invention is not
limited to the method or detail of construction, fabrication,
material, application or use described an illustrated herein.
Indeed, any suitable variation of fabrication, use, or application
is contemplated as an alternative embodiment, and thus is within
the spirit and scope of the invention.
[0136] It is further intended that any other embodiments of the
present invention that result from any changes in application or
method of use or operation, configuration, method of manufacture,
shape, size, or material, which are not specified within the
detailed written description or illustrations contained herein yet
would be understood by one skilled in the art, are within the scope
of the present invention.
[0137] Finally, those of skill in the art will appreciate that the
invented method, system and apparatus described and illustrated
herein may be implemented in software, firmware or hardware, or any
suitable combination thereof. Preferably, the method and apparatus
are implemented in a combination of the three, for purposes of low
cost and flexibility. Thus, those of skill in the art will
appreciate that embodiments of the methods and system of the
invention may be implemented by a computer or microprocessor
process in which instructions are executed, the instructions being
stored for execution on a computer-readable medium and being
executed by any suitable instruction processor.
[0138] Accordingly, while the present invention has been shown and
described with reference to the foregoing embodiments of the
invented apparatus, it will be apparent to those skilled in the art
that other changes in form and detail may be made therein without
departing from the spirit and scope of the invention as defined in
the appended claims.
* * * * *
References